



'''simple docstring'''



import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Optional[Any]						  =					(DPMSolverSDEScheduler,)
						_lowerCAmelCase  : int						  =					1_0







						def        __lowercase     (	self :   List[str]					,     **lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      {
													    '''num_train_timesteps''': 11_00,
													    '''beta_start''': 0.0001,
													    '''beta_end''': 0.02,
													    '''beta_schedule''': '''linear''',
													    '''noise_sampler_seed''': 0,
													}

													config.update(**lowerCAmelCase__					)
													return config







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for timesteps in [10, 50, 1_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]					,     [0.0002, 0.002, 0.02]					):
																				self.check_over_configs(beta_start=lowerCAmelCase__					,     beta_end=lowerCAmelCase__					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=lowerCAmelCase__					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=lowerCAmelCase__					)







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.get_scheduler_config()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler_class(**lowerCAmelCase__					)

													scheduler.set_timesteps(self.num_inference_steps					)

													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE       :   List[str]         =      sample.to(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   Dict         =      scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      output.prev_sample

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 167.47_8210_4492_1875					) < 1e-2
																				assert abs(result_mean.item() - 0.2178_7059_6456_5277					) < 1e-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 171.59_3521_1181_6406					) < 1e-2
																				assert abs(result_mean.item() - 0.2_2342_9068_9229_9652					) < 1e-3
													else:
																				assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
																				assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Any         =      self.get_scheduler_config(prediction_type='''v_prediction'''					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler_class(**lowerCAmelCase__					)

													scheduler.set_timesteps(self.num_inference_steps					)

													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   List[str]         =      self.dummy_sample_deter * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE       :   List[str]         =      sample.to(lowerCAmelCase__					)

													for i, t in enumerate(scheduler.timesteps					):
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   Tuple         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Dict         =      output.prev_sample

													SCREAMING_SNAKE_CASE       :   str         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   str         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 124.77_1492_0043_9453					) < 1e-2
																				assert abs(result_mean.item() - 0.1_6226_2890_1481_6284					) < 1e-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 128.1_6633_6059_5703					) < 1e-2
																				assert abs(result_mean.item() - 0.1_6688_3260_0116_7297					) < 1e-3
													else:
																				assert abs(result_sum.item() - 119.8_4875_4882_8125					) < 1e-2
																				assert abs(result_mean.item() - 0.1560_5306_6253_6621					) < 1e-3







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Any         =      self.get_scheduler_config()
													SCREAMING_SNAKE_CASE       :   List[str]         =      scheduler_class(**lowerCAmelCase__					)

													scheduler.set_timesteps(self.num_inference_steps					,     device=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.dummy_sample_deter.to(lowerCAmelCase__					) * scheduler.init_noise_sigma

													for t in scheduler.timesteps:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   str         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Any         =      output.prev_sample

													SCREAMING_SNAKE_CASE       :   str         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 167.46_9573_9746_0938					) < 1e-2
																				assert abs(result_mean.item() - 0.2_1805_9346_0798_2635					) < 1e-3
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 171.59_3536_3769_5312					) < 1e-2
																				assert abs(result_mean.item() - 0.2_2342_9083_8241_5771					) < 1e-3
													else:
																				assert abs(result_sum.item() - 162.52_3834_2285_1562					) < 1e-2
																				assert abs(result_mean.item() - 0.211_6195_7085_1326					) < 1e-3







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE       :   Any         =      self.get_scheduler_config()
													SCREAMING_SNAKE_CASE       :   Any         =      scheduler_class(**lowerCAmelCase__					,     use_karras_sigmas=lowerCAmelCase__					)

													scheduler.set_timesteps(self.num_inference_steps					,     device=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_model()
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_sample_deter.to(lowerCAmelCase__					) * scheduler.init_noise_sigma
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      sample.to(lowerCAmelCase__					)

													for t in scheduler.timesteps:
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      scheduler.scale_model_input(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(lowerCAmelCase__					,     lowerCAmelCase__					)

																				SCREAMING_SNAKE_CASE       :   List[Any]         =      scheduler.step(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      output.prev_sample

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.sum(torch.abs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      torch.mean(torch.abs(lowerCAmelCase__					)					)

													if torch_device in ["mps"]:
																				assert abs(result_sum.item() - 176.66_9741_3574_2188					) < 1e-2
																				assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
													elif torch_device in ["cuda"]:
																				assert abs(result_sum.item() - 177.63_6535_6445_3125					) < 1e-2
																				assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2
													else:
																				assert abs(result_sum.item() - 170.3_1352_2338_8672					) < 1e-2
																				assert abs(result_mean.item() - 0.2_3003_8727_3098_1811					) < 1e-2









'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




